Call for Papers
Machine and deep learning techniques become increasingly popular and achieve remarkable success nowadays in many application domains, e.g., speech recognition, bioinformatics and computer vision. Machine learning is capable to exploit the hidden relationship from voluminous input data to complicated system outputs, especially for some advanced techniques, like the deep learning. Moreover, some other techniques, e.g., reinforcement learning, could further adapt the learning results in the new environments to evolve automatically. These features perfectly match the complex, dynamic and time-varying nature of today’s networking systems. This workshop focus on discussing a new networking paradigm — knowledge centric networking (KCN). The key insight is applying the emerging machine or deep learning techniques and leveraging the big data to derive remarkable knowledge for benefiting the system design, instead of viewing them as undesired burdens. On the other hand, benefiting from the IoT, big data is mature already for an immediate usage in various networking systems. Therefore, we can envision KCN as a rewarding solution to leverage learning techniques on the pervasively available data contents to create knowledge and facilitate the networking system designs.
This workshop present related state-of-the-art research in KCN. Both theoretical and system papers on will be considered, to present novel contributions in the field of machine learning and deep learning applied to networking, including scalable analytic techniques and frameworks capable of collecting and analyzing both online and offline massive datasets, open issues related to the application of machine learning into communications and networking problems and to share new ideas and techniques for machine learning in communication systems and networks. The topics of interest include (but not limited to):
- KCN network architecture design
- Application-driven KCN architecture design
- Machine learning and data mining in KCN
- Transfer learning and reinforcement learning for networking system
- Bio-inspired learning for networking and communications
- Protocol design and optimization using machine learning
- Data analytics for network and wireless measurements mining
- Big data analysis frameworks for network monitoring data
- Network anomaly diagnosis through big networking data and wireless
- Machine learning and big data analytics for network management
- Big data analytics and visualization for traffic analysis
- Resource allocation for virtualized networks using machine learning
- Fault-tolerant network protocols using machine learning
- Experiences and best-practices using machine learning in operational networks
- Security, performance, and monitoring applications in KCN
- Blockchain enabled network infrastructure and mechanisms for KCN
- Edge/fog computing for communication, computation and caching in KCN